
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import TFXLMRobertaModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 					lowerCamelCase__				(							unittest.TestCase):




	'''simple docstring'''




	@slow
	def   _lowerCamelCase							(   self						:Optional[Any]		)						->					str:
								__UpperCamelCase      :  List[str]       =   TFXLMRobertaModel.from_pretrained("jplu/tf-xlm-roberta-base"		)

								__UpperCamelCase      :  int       =   {
								    "input_ids": tf.convert_to_tensor([[0, 2_6_4_6, 1_0_2_6_9, 8_3, 9_9_9_4_2, 2]]	,  dtype=tf.intaa		),  # "My dog is cute"
								    "attention_mask": tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]	,  dtype=tf.intaa		),
								}

								__UpperCamelCase      :  Union[str, Any]       =   model(a		)["last_hidden_state"]
								__UpperCamelCase      :  int       =   tf.TensorShape((1, 6, 7_6_8)		)
								self.assertEqual(output.shape	,  a		)
								# compare the actual values for a slice.
								__UpperCamelCase      :  Tuple       =   tf.convert_to_tensor(
								    [
								        [
								            [0.0681762, 0.10894451, 0.06772504],
								            [-0.06423668, 0.02366615, 0.04329344],
								            [-0.06057295, 0.09974135, -0.00070584],
								        ]
								    ]	,  dtype=tf.floataa	,  )

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()	,  expected_slice.numpy()	,  atol=1E-4		)		)

from __future__ import annotations




class 					lowerCamelCase__				:




	'''simple docstring'''




	def __init__(   self						:Dict	,  a						:str	,  a						:str		)						->					Union[str, Any]:
								__UpperCamelCase  ,		__UpperCamelCase      :  Optional[int]       =   text, pattern
								__UpperCamelCase  ,		__UpperCamelCase      :  Tuple       =   len(a		), len(a		)







	def   _lowerCamelCase							(   self						:Any	,  a						:str		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if char == self.pattern[i]:
																						return i
								return -1







	def   _lowerCamelCase							(   self						:str	,  a						:int		)						->					int:

								for i in range(self.patLen - 1	,  -1	,  -1		):
															if self.pattern[i] != self.text[current_pos + i]:
																						return current_pos + i
								return -1







	def   _lowerCamelCase							(   self						:Union[str, Any]		)						->					list[int]:
								# searches pattern in text and returns index positions
								__UpperCamelCase      :  Any       =   []
								for i in range(self.textLen - self.patLen + 1		):
															__UpperCamelCase      :  List[Any]       =   self.mismatch_in_text(a		)
															if mismatch_index == -1:
																						positions.append(a		)
															else:
																						__UpperCamelCase      :  Any       =   self.match_in_pattern(self.text[mismatch_index]		)
																						__UpperCamelCase      :  Dict       =   (
																						    mismatch_index - match_index
																						)  # shifting index lgtm [py/multiple-definition]
								return positions
lowercase    :   Any						=			'ABAABA'
lowercase    :   str						=			'AB'
lowercase    :   str						=			BoyerMooreSearch(text, pattern)
lowercase    :   Union[str, Any]						=			bms.bad_character_heuristic()

if len(positions) == 0:
		print('No match found')
else:
		print('Pattern found in following positions: ')
		print(positions)